Gil Sadeh

Computer Vision and Pattern Recognition · 2014–2015

A researcher in the Science corpus: 3 papers; trusts 693 researchers and is trusted by 362 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 114 citations into it; #2,131 of 21,575 by trust here, #5,507 by citations
• Artificial Intelligence — 66 citations into it; #6,245 of 35,345 by trust here, #9,092 by citations
• Natural Language Processing — 40 citations into it
• Multimodal Learning — 35 citations into it; #340 of 1,211 by trust here, #671 by citations
• Model Architectures — 15 citations into it
• Representation & Self-Supervised Learning — 14 citations into it; #1,134 of 3,145 by trust here, #1,543 by citations
• Evaluation & Benchmarks — 10 citations into it
• Media Technology — 10 citations into it; #2,293 of 3,621 by trust here, #1,859 by citations

Whom Gil Sadeh trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Karen Simonyan
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Yann LeCun
5. Andrew Zisserman
6. Florent Perronnin
7. Ilya Sutskever
8. Alex Krizhevsky
9. Radford M. Neal
10. Tomas Mikolov

Who trusts Gil Sadeh overall

1. Yan Huang
2. Antoine Miech
3. Bryan A. Plummer
4. Zhedong Zheng
5. Jianfeng Dong
6. Liang Wang
7. Liwei Wang
8. Svetlana Lazebnik
9. Liang Zheng
10. Yuxin Peng

Papers from Gil Sadeh's perspective

Ordered by how strongly the researchers Gil Sadeh trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Associating neural word embeddings with deep image representations using Fisher Vectors (2015) — 302 citations
• RNN Fisher Vectors for Action Recognition and Image Annotation (2015) — 172 citations
• Fisher Vectors Derived from Hybrid Gaussian-Laplacian Mixture Models for Image Annotation (2014) — 103 citations